Computing Nash equilibria in bimatrix games: GPU-based parallel support enumeration

Rampersaud, Safraz; Mashayekhy, Lena; Grosu, Daniel

doi:10.1109/pccc.2012.6407775

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Some techniques can be used to reduce the computation time. First, parallel computing can be employed when constructing the game tree since each path in the game tree are independent [44]. Second, some methods, e.g., the parallel branch & bound method [45] and the modified Benders decomposition method [46], can be used to accelerate the convergence of the solution for each path.…”

Section: Discussionmentioning

confidence: 99%

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Dynamic Game-Based Maintenance Scheduling of Integrated Electric and Natural Gas Grids With a Bilevel Approach

Wang

Hou

et al. 2018

IEEE Trans. Power Syst.

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P u ,t , P u ,t−1 Power generation of generator u at t and t − 1, respectively.Power from bus b to bus b at t. P S n,t , P S n ,t Pressure of gas nodes n and n at t. o u ,t Binary variable to indicate if generator u is started up at t. θ b,t , θ b ,t Phase angles of buses b and b at t. η k p ,t , η k p,tBinary variables to indicate if the piecewise linear function k is selected. '1' denotes 'selected', and '0' denotes 'nonselected'. π n,t , π n ,t

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Section: Discussionmentioning

confidence: 99%

“…where (44) enforces power balance at each bus in each time period. (45) is the constraint of load shedding.…”

Section: Lower Level Model 1) Objective Functionmentioning

confidence: 99%

Dynamic Game-Based Maintenance Scheduling of Integrated Electric and Natural Gas Grids With a Bilevel Approach

Wang

Hou

et al. 2018

IEEE Trans. Power Syst.

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“…Like Merge and Odd-Even Sort, Enumeration Sort performs far better in small applications where it can be better parallelized [11], and fails to take advantage of KNN's k parameter. It has lower resource overhead than Merge Sort, which is likely why it performs better than the worst case Merge Sort latencies.…”

Section: Hls Simulationmentioning

confidence: 99%

An Efficient SDN Architecture for Smart Home Security Accelerated by FPGA

Gordon¹,

Park²,

Tushir³

et al. 2021

Preprint

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With the rise in Internet of Things (IoT) devices, home network management and security are becoming complex.There is an urgent requirement to make smart home network management efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is further enhanced by offloading the computation-intensive KNN model to Field Programmable Gate Arrays (FPGA), which offers parallel processing power of GPU platforms at lower costs and higher efficiencies, and can be used to accelerate time-sensitive tasks. The proposed parallelization and implementation of KNN on FPGA are achieved by using the Vivado Design Suite from Xilinx and High-Level Synthesis (HLS). When optimized with 10-fold cross-validation, the proposed solution for KNN consistently exhibits the best performances on FPGA when compared with four alternative KNN instances (i.e., 78% faster than the parallel Bubble Sortbased implementation and 99% faster than the other three sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 milliseconds on FPGA compared to 57 seconds on a CPU platform.

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“…In this work we investigate the problem of multicriteria management and scheduling of malleable tasks [4]. These tasks provide a high level of granularity and parallelisation and have numerous practical applications in scientific and real-life computations [5], [6], [7]. In contrast to previous work that restricted the optimisation criteria for scheduling, the present work investigates algorithms to schedule and efficiently execute malleable computing tasks with high granularity while taking into account multiple optimisation criteria such as resource cost and computation time.…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria malleable task management for hybrid-cloud platforms

Caron¹,

Assunção²

2016

2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)

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The use of large distributed computing infrastructure is a means to address the ever increasing resource demands of scientific and commercial applications. The scale of current large-scale computing infrastructures and their heterogeneity make scheduling applications an increasingly complex task. Cloud computing minimises the heterogeneity by using virtualisation mechanisms, but poses new challenges to middleware developers, such as the management of virtualisation, elasticity and economic models. In this context, this work proposes algorithms for efficient scheduling and execution of malleable computing tasks with high granularity while taking into account multiple optimisation criteria such as resource cost and computation time. We focus on hybrid platforms that comprise both clusters and cloud providers. We define and formalise the main aspects of the problem, introduce the difference between local and global scheduling algorithms and evaluate their efficiency using discrete-event simulation.

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Computing Nash equilibria in bimatrix games: GPU-based parallel support enumeration

Cited by 4 publications

References 18 publications

Dynamic Game-Based Maintenance Scheduling of Integrated Electric and Natural Gas Grids With a Bilevel Approach

Dynamic Game-Based Maintenance Scheduling of Integrated Electric and Natural Gas Grids With a Bilevel Approach

An Efficient SDN Architecture for Smart Home Security Accelerated by FPGA

Multi-criteria malleable task management for hybrid-cloud platforms

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